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Abstract Purpose of ReviewTerrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO$$^{2}$$ uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes. Recent FindingsCross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming. SummaryWhile SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections.
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This content will become publicly available on August 7, 2025
Comparing assumptions and applications of dynamic vegetation models used in the Arctic-Boreal zone of Alaska and Canada
Abstract Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development.
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- PAR ID:
- 10557860
- Publisher / Repository:
- Environmental Research Letters
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 19
- Issue:
- 9
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 093003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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